
\begin{table}[ht]
\scriptsize
\caption{Results from the models used to estimate the effects in \Cref{fig:fg5}}\label{tab:fg5}
\begin{center}
\begin{tabular}{lccccc}
\hline\hline
 & \multicolumn{2}{c}{Democratic Appointees} & & \multicolumn{2}{c}{Republican Appointees}\\\cline{2-3}\cline{5-6}
 & (1) & (2) &  & (3) & (4)\\
\hline
Nontraditional Appointees & --0.01 & --0.002 &  & --0.034** & 0.022\\
 & (0.011) & (0.011) &  & (0.011) & (0.012)\\
\hline
Outcome & Stlmt. & Deft. &  & Stlmt. & Deft.\\
 &  & Wins &  &  & Wins\\
Cases & 26,033 & 26,033 &  & 17,411 & 17,411\\
Treatment Judges & 215 & 215 &  & 104 & 104\\
Control Judges & 174 & 174 &  & 168 & 168\\
Randomization Blocks & \checkmark & \checkmark &  & \checkmark & \checkmark\\
Min. Units/Trt. Arm & 5 & 5 &  & 5 & 5\\
Appt. Pres. Controls &  &  &  &  & \\
\hline\hline
\end{tabular}
\end{center}
{\scriptsize \textit{Notes:} All models include district-division-year fixed effects, which we refer to as our ``randomization blocks.'' We also use an adjustment proposed by Lin (2013) for all control variables (see main text). Standard errors are clustered by judge. Statistical significance is indicated by stars: * $p$ < 0.05, ** $p$ < 0.01 and *** $p$ < 0.001. }
\end{table}
